Adaptation and Coevolution on an Emergent Global Competitive Landscape

TL;DR

This paper explores how dynamic fitness landscape models, inspired by biological evolution, can enhance economic theory, especially in understanding technology substitution and firm adaptation in competitive markets.

Contribution

It introduces the application of dynamic fitness landscape models to economic theory, bridging biological evolution concepts with market competition and technological change.

Findings

Dynamic models better capture firm adaptation over time.

Fitness landscapes explain technology substitution processes.

Evolutionary approaches provide new insights into market dynamics.

Abstract

Notions of Darwinian selection have been implicit in economic theory for at least sixty years. Richard Nelson and Sidney Winter have argued that while evolutionary thinking was prevalent in prewar economics, the postwar Neoclassical school became almost entirely preoccupied with equilibrium conditions and their mathematical conditions. One of the problems with the economic interpretation of firm selection through competition has been a weak grasp on an incomplete scientific paradigm. As I.F. Price notes, "The biological metaphor has long lurked in the background of management theory largely because the message of 'survival of the fittest' (usually wrongly attributed to Charles Darwin rather than Herbert Spencer) provides a seemingly natural model for market competition (e.g. Alchian 1950, Merrell 1984, Henderson 1989, Moore 1993), without seriously challenging the underlying paradigms of what an organisation is." In this paper we examine the application of dynamic fitness landscape models to economic theory, particularly the theory of technology substitution, drawing on recent work by Kauffman, Arthur, McKelvey, Nelson and Winter, and Windrum and Birchenhall. In particular we use Professor Post's early work with John Holland on the genetic algorithm to explain some of the key differences between static and dynamic approaches to economic modeling.